Prioritizing spatially aggregated cost-effective sites in natural reserves to mitigate human-induced threats: A case study of the Qinghai Plateau, China

Anthropogenic activities often lead to the degradation of valuable natural habitats. Many efforts have been taken to counteract this degradation process, including the mitigation of human-induced stressors. However, knowing-doing gaps exist in stakeholder’s decision-making of prioritizing sites to allocate limited resources in these mitigation activities in both spatially aggregated and cost-effective manner. In this study, we present a spatially explicit prioritization framework that integrates basic cost effectiveness analysis (CEA) and spatial clustering statistics. The advantages of the proposed framework lie in its straightforward logic and ease of implementation to assist stakeholders in the identification of threat mitigation actions that are both spatially clumped and cost-effective using innovative prioritization indicators. We compared the utility of three local autocorrelation-based clustering statistics, including local Moran’s I, Getis-Ord Gi*, and AMOEBA, in quantifying the spatial aggregation of identified sites under given budgets. It is our finding that the CEA method produced threat mitigation sites that are more cost-effective but are dispersed in space. Spatial clustering statistics could help identify spatially aggregated management sites with only minor loss in cost effectiveness. We concluded that integrating basic CEA with spatial clustering statistics provides stakeholders with straightforward and reliable information in prioritizing spatially clustered cost-effective actions for habitat threat mitigation.

We define the biodiversity benefits as the improvement of habitat's capability to support all levels of biodiversity.Specifically, we calculated the expected biodiversity benefit in a hexagon unit by summing up the increase in habitat quality (HQ) and decrease in habitat degradation (HD) after removing specific human land uses within or surrounding this unit.The HQ and HD were calculated using the biodiversity model (available as a toolbox in the ArcGIS software [1]) provided by the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) modeling framework (https://www.naturalcapitalproject.org/invest/ ) based on four factors [2][3][4][5].The first factor is the relative suitability (Hj) of each type of habitat to sustain biodiversity.Obviously, not all habitats have the same capability and condition to accommodate various species.For instance, tropical forest is a richerbiodiversity habitat than alpine grassland.Hj ranges from 0 to 1, where Hj = 1 indicates the most suitable habitat for species.The second factor is the relative impact intensity (Wr) of each threat to its surrounding habitats.Not all human-induced threats degrade habitats at the same degree.A larger Wr means more harm to its surrounding habitats.For example, urban areas are more detrimental to neighboring habitats than farmland.The third factor is the relative sensitivity (Sjr) of each habitat type to each threat.Habitats respond differently to various threats.Habitats with a more integrated and hierarchical structure tend to be more robust and resilient, thus showing less sensitivity to surrounding threats, and vice versa.The sensitivity of habitats to threats is often defined based on prior knowledge of researchers who well comprehend general principles in landscape ecology for biodiversity conservation.The fourth factor is the distance of habitats to the source of threats.Habitats far from threats sustain less impacts than habitat proximate to these threats.When the distance between habitats and threats increases, the impact of these threats decays.The model utilizes an exponential decay function to concern the nonlinear relationship between the impacts on habitats and the distance from threats.A maximum effective distance ( ) of each type of threat is given as a threshold, over which the sensitivity of habitats to threats equals to zero.Generally, landscapes which carry intensive human activities and absorb substantial human investment are often seen as source of habitat threats.The more intensive and extensive human activities are, the more the habitats are threatened and degraded (leading to more biodiversity loss).Based on the above principle, the following method (Eq. 1) was applied to calculate the habitat degradation in each cell x.
where HDxj represents the degradation degree of habitat at cell x with a landscape type j inside.r stands for threatening factor.R indicates the total number of threatening factors.Wr represents the impact intensity of threatening factor r. Yr is the number of cells in raster map of each threatening factor r. ry indicates the number of cells of threatening factor in each cell of habitat.Sjr stands for a habitat's sensitivity to threatening factor j, and irxy represents the impact of threatening factor r that originates in cell y on habitat in cell x, which is calculated using Eq. 2 as follow.
where  represents the relative quality of habitat cell x. stands for the suitability of habitat type j. k is the half-saturation constant and is often set to half of the largest value of  .So the model should be repeated to get max ( ).z is a scaling parameter (often set to 2.5).More technical details and input data for the biodiversity model of InVEST are described by [6][7][8] Whether a land is treated as wildlife habitat depends on the species of interest.In this study, we focus on the capability of a habitat to support all levels of biodiversity instead of any particular species.Generally, landscapes with high-quality habitats are more likely to preserve all levels of species in contrast to those consisting of habitats that are degraded by intensive human land use activities [9].According to [10], land use/cover types with more human intervention such as urban, village, mining site, tourist attraction, farmland, primary road, railway and local road are regarded as habitat threatening sources.Natural and semi-natural land covers such as forest, shrub land, grassland, orchard, wetland, and farmland are often viewed as habitats for wildlife.For the calculation of HQ and HD, parameters such as the impact intensity and the maximum influence distance of threats, and habitat's sensitivity to each threat were extensively reported in the literature [3,4,7,11,12] (see Table S3-10 in supplementary material).Parameters suggested by the InVEST biodiversity model [2] were also taken as references in this study.Then, minor adjustments were given to these parameters based on knowledge from experts that have long been working in the study area on evaluating the environmental impacts from land use activities.Table S1 and S2 show the parameters used in this study.
Table S1.Intensity and the maximum influence distance of threatening factors (drmax: maximum effective distance of each threat type, which is a threshold over which the sensitivity of habitats to threats equals to zero.See Eq.S1 for Wr) International organizations and governments of Qinghai Province have launched multiple ecological restoration projects to reverse the trend of habitat degradation and biodiversity loss in the past decades.Among these projects, a set of measures were used to mitigate human-induced threats that degraded local habitats [13].We focus our analysis on the removal of specific human land uses in PAs, including farmland, rural residential land, and local roads.These human land uses are the major anthropogenic threats to natural habitat in PAs, and are more realistic to be removed compared with other land uses (e.g., mining sites and tourism land) in terms of social acceptability and financial constraints.To be mentioned, the remaining mining sites that impact the habitat qualities in the PAs play an important role in producing various rare earth metals which are important elements for many industrial products.So it is economically unrealistic to ban these mining sites in recent years.The quantity and spatial distribution of farmland, rural residential land, local roads, and mining sites inside and around the PAs are shown in Table S11 in supplementary materials.Based on these mitigation actions, an adjusted land use/cover map was created by removing these three human land uses.This scenario-based land use/cover map was imported into the biodiversity model to spatially estimate HQ and HD with model parameters in Table S1 and S2.Then, expected biodiversity benefit in each hexagon unit was calculated as follow.
where  stands for the expected biodiversity benefit in hexagon unit i. Ω is the number of cells within hexagon unit i.  , and  , represent the current habitat degradation and quality in cell x, respectively. , and  , show the simulated habitat degradation and quality in cell x if the specific land uses around and within hexagon unit i are eliminated.
In general, the parameter values that were used in the biodiversity model were set mostly according to the study conducted by Polasky [11] (see Table S3 and S4 for parameter values in Polasky's study).If parameter values corresponding to a land use/cover type and a threat factor can not be found in Polasky's study, we refer to the other studies [3,4,7,12] that present these parameters as supplement (see Table S5 -10 for parameter values in these studies).Also, minor adjustments were gave to parameters found in these studies according to expert knowledge and our own understanding about the land use/cover and landscape characteristics in the Qinghai Plateau.The experts are mainly from the provincial and county-level Department of Land and Resources Management in the Qinghai Plateau.These experts have long been working in the study area on estimation of the environmental impacts from land use activities.To be mentioned, the HQ and HD indices focus on reflecting the relative conditions in different habitat cells, so only the relative magnitude of the parameter values matters when computing these two indices.We discussed about the relative magnitude of habitat suitability, sensitivity of habitat to different threats, and weights of threat with these experts when conducting field survey works in the study area.

Figure S1 -
Figure S1-Hexagonal units in the protected areas

Figure S3 -
Figure S3-The distribution of farmland, rural residential land, local roads, and mining sites within and around the PAs

Table S2 .
Habitat suitability and its sensitivity to different threatening factors (see Eq. S1 for Sjr and Eq.S2 for Hj)

Table S3 -
[11]es for habitat suitability and sensitivity to different threats[11]Note: The asterisks denote natural lands that are managed for a variety of economic, environmental, and recreational uses.

Table S4 -
[11]es for relative weights and the maximum influence distance of threatening factors[11]Source:Polasky, S.; Nelson, E.; Pennington, D.; Johnson, K. A., The Impact of Land-Use Change on Ecosystem Services, Biodiversity and Returns to Landowners: A Case Study in the State of Minnesota.Environ Resour Econ 2011, 48, (2), 219-242.

Table S11 .
The quantity of farmland, rural residential land, local roads, and mining sites within and around the PAs